pyFDA is a tool for designing and analysing discrete time filters written in python with a graphical user interface.
Project description
pyFDA is a GUI based tool in Python / Qt for analysing and designing discrete time filters. The capability for generating Verilog and VHDL code for the designed and quantized filters will be added in the next release.
Prerequisites
Besides standard python libraries, the project builds on the following libraries:
numpy
scipy
matplotlib
docutils
pyQt4
Optional: xlwt and / or XlsxWriter for exporting filter coefficients as *.xls(x) files
Installing and starting pyFDA
Download the zip file and extract it to a directory of your choice. Install it either to your <python>/Lib/site-packages subdirectory using
>> python setup.py install
or run it where you have installed the python source files using (for testing / development)
>> python setup.py develop
In both cases, start scripts pyfdax* are created in <python>/Scripts.
For development, you can also run pyFDA using:
In [1]: %run -m pyfda.pyfdax # IPython or >> python -m pyfda.pyfdax # plain python interpreter
or files from pyFDA using e.g.:
In [2]: %run -m pyfda.input_widgets.input_pz # IPython or >> python -m pyfda.input_widgets.input_pz # plain python interpreter
Customization
The layout and some default paths can be customized using the file pyfda/pyfda_rc.py.
Features
- Filter design
Design methods from scipy.signal: Equiripple, Firwin, Butterworth, Elliptic, Chebychev 1 and Chebychev 2
Remember all specifications when changing filter design methods
Fine-tune manually the filter order and corner frequencies calculated by minimum order algorithms
Compare filter designs for a given set of specifications and different design methods
Filter coefficients and poles / zeroes can be displayed, edited and quantized
- Clearly structured GUI
only widgets needed for the currently selected design method are visible
enhanced matplotlib NavigationToolbar (nicer icons, additional functions)
- Common interface for all filter design methods:
specify frequencies as absolute values or normalized to sampling or Nyquist frequency
specify ripple and attenuations in dB, as voltage or as power ratios
enter expressions like exp(-pi/4 * 1j) with the help of the library simpleeval (https://pypi.python.org/pypi/simpleeval) (included in source files)
- Graphical Analyses
Magnitude response (lin / power / log) with optional display of specification bands, phase and an inset plot
Phase response (wrapped / unwrapped)
Group delay
Pole / Zero plot
Impulse response and step response (lin / log)
3D-Plots (|H(f)|, mesh, surface, contour) with optional pole / zero display
- Modular architecture, facilitating the implementation of new filter design and analysis methods
Filter design files not only contain the actual algorithm but also dictionaries specifying which parameters and standard widgets have to be displayed in the GUI.
Special widgets needed by design methods (e.g. for choosing the window type in Firwin) are included in the filter design file, not in the main program
Filter design files can be added and edited without changing or even restarting the program
- Saving and loading
Save and load filter designs in pickled and in numpy’s NPZ-format
Export coefficients and poles/zeros as comma-separated values (CSV), in numpy’s NPZ-format, in Excel (R) or in Matlab (R) workspace format
Display help files (own / Python docstrings) as rich text
Runs under Python 2.7 and Python 3.3 … 3.5
Why yet another filter design tool?
Education: There is a very limited choice of user-friendly, license-free tools available to teach the influence of different filter design methods and specifications on time and frequency behaviour. It should be possible to run the tool without severe limitations also with the limited resolution of a beamer.
Show-off: Demonstrate that Python is a potent tool for digital signal processing applications as well. The interfaces for textual filter design routines are a nightmare: linear vs. logarithmic specs, frequencies normalized w.r.t. to sampling or Nyquist frequency, -3 dB vs. -6 dB vs. band-edge frequencies … (This is due to the different backgrounds and the history of filter design algorithms and not Python-specific.)
Fixpoint filter design for uCs: Recursive filters have become a niche for experts. Convenient design and simulation support (round-off noise, stability under different quantization options and topologies) could attract more designers to these filters that are easier on hardware resources and much more suitable e.g. for uCs.
Fixpoint filter design for FPGAs: Especially on low-budget FPGAs, multipliers are expensive. However, there are no good tools for designing and analyzing filters requiring a limited number of multipliers (or none at all) like CIC-, LDI- or Sigma-Delta based designs.
HDL filter implementation: Implementing a fixpoint filter in VHDL / Verilog without errors requires some experience, verifying the correct performance in a digital design environment with very limited frequency domain simulation options is even harder. The Python module myHDL (http://myhdl.org) can automate both design and verification.
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